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Data Quality Assurance Engineer Jobs in Reston, VA

Data QA Engineer

Bethesda, MD

$122K - $146K/yr

We are is looking for talented, enthusiastic senior data engineers who share our passion for big ... Qualifications Minimum Qualifications * 5+ years of work experience in QA, preferably in data or ...

Data QA Engineer

Bethesda, MD ยท On-site +1

$122K - $146K/yr

We are is looking for talented, enthusiastic senior data engineers who share our passion for big ... Qualifications Minimum Qualifications * 5+ years of work experience in QA, preferably in data or ...

Data Validation & ETL Testing * Validate ETL/ELT pipelines to ensure accurate ingestion ... AI-Driven Quality Engineering * Apply AI/ML and generative AI tools to enhance QA processes ...

Everforth ECS Federal is seeking a Quality Assurance Engineer to support a mission-focused federal IT program in Washington DC. Please Note: This position is contingent upon contract award. Join ...

Software Quality Assurance Engineer FULL-TIME, WASHINGTON, D.C. About the job Throne aims to provide peace of mind to those that venture out each day -- making it so no one has to worry about finding ...

Software Quality Assurance Engineer FULL-TIME, WASHINGTON, D.C. About the job Throne aims to provide peace of mind to those that venture out each day -- making it so no one has to worry about finding ...

QA Engineer

Mclean, VA ยท On-site

Job Title: QA Engineer Company: BLN24 About Us:We find strength in teamwork-a better you is a ... Validate end-to-end workflows, including form submission, data processing, and notice/document ...

Validate data accuracy and reports * Maintain test documentation * Collaborate with project teams Required Qualifications * 2 3 years of experience in QA or testing * Knowledge of SDLC and STLC

QA Engineer

Mclean, VA ยท On-site

BLN24 is seeking a QA Engineer to support the development and maintenance of Adobe Experience ... Validate end-to-end workflows, including form submission, data processing, and notice/document ...

Quality Assurance Engineer

Gaithersburg, MD ยท Remote

$84K - $109K/yr

We are hiring a Quality Assurance Engineer to support our rapid customer growth. Do you thrive on ... Effective communicator with data at all levels of the organization * Effective facilitator of cross ...

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Data Quality Assurance Engineer information

See Reston, VA salary details

$19

$50

$81

How much do data quality assurance engineer jobs pay per hour?

As of Jun 25, 2026, the average hourly pay for data quality assurance engineer in Reston, VA is $50.50, according to ZipRecruiter salary data. Most workers in this role earn between $39.76 and $57.79 per hour, depending on experience, location, and employer.

What is the difference between Data Quality Assurance Engineer vs Data Analyst?

AspectData Quality Assurance EngineerData Analyst
Primary FocusEnsuring data accuracy, integrity, and quality through testing and validation processesAnalyzing data to identify trends, generate reports, and support decision-making
Skills & CertificationsKnowledge of data testing tools, SQL, data management, and quality standardsProficiency in data analysis tools, SQL, Excel, and visualization software
Work EnvironmentOften part of data engineering or QA teams within IT or data departmentsTypically within business intelligence, marketing, or analytics teams

While both roles work with data, the Data Quality Assurance Engineer focuses on validating and maintaining data quality, whereas the Data Analyst interprets data to provide insights. They often collaborate but serve different functions in data management and analysis processes.

What are the key skills and qualifications needed to thrive as a Data Quality Assurance Engineer, and why are they important?

To thrive as a Data Quality Assurance Engineer, you need strong analytical skills, attention to detail, and a background in computer science or a related field. Familiarity with SQL, data profiling tools, automation frameworks, and certifications like ISTQB are commonly required. Excellent problem-solving, communication, and collaboration skills set top performers apart in this role. These competencies are vital for ensuring data integrity, driving process improvements, and supporting business decision-making with reliable information.

What does a Data Quality Assurance Engineer do?

A Data Quality Assurance Engineer is responsible for ensuring the accuracy, consistency, and reliability of data within an organization. They design and implement tests, validation processes, and quality checks to identify and resolve data errors or inconsistencies. Their work helps maintain high-quality data standards, which is crucial for informed decision-making and effective business operations. Additionally, they collaborate with data engineers and analysts to establish data quality metrics and best practices.

What are some common challenges faced by Data Quality Assurance Engineers when working with large datasets?

Data Quality Assurance Engineers often encounter challenges such as data inconsistency, incomplete records, and discrepancies across multiple data sources when handling large datasets. Ensuring data integrity requires meticulous validation and a strong understanding of both automated and manual testing techniques. Collaboration with data engineers, analysts, and business stakeholders is essential to identify root causes of quality issues and to implement effective solutions. Staying adaptable and detail-oriented helps address evolving data requirements and maintain high standards of data accuracy.
What are popular job titles related to Data Quality Assurance Engineer jobs in Reston, VA? For Data Quality Assurance Engineer jobs in Reston, VA, the most frequently searched job titles are:
What job categories do people searching Data Quality Assurance Engineer jobs in Reston, VA look for? The top searched job categories for Data Quality Assurance Engineer jobs in Reston, VA are:
What cities near Reston, VA are hiring for Data Quality Assurance Engineer jobs? Cities near Reston, VA with the most Data Quality Assurance Engineer job openings:
Infographic showing various Data Quality Assurance Engineer job openings in Reston, VA as of June 2026, with employment types broken down into 93% Full Time, and 7% Contract. Highlights an 57% In-person, and 43% Remote job distribution, with an average salary of $105,044 per year, or $50.5 per hour.
Quality Assurance Engineer

Quality Assurance Engineer

Anika Systems

Leesburg, VA โ€ข Remote

Full-time

Posted 2 days ago


Job description

Anika Systems is seeking a highly technical Quality Assurance Engineer with strong development, SQL, and Python expertise to support enterprise data platforms for federal clients. This is not a traditional manual QA role and this position requires a developer mindset, focused on automation, data validation, and platform reliability across modern cloud-based architectures.
The ideal candidate will design and implement automated testing frameworks for ETL pipelines, Apache Iceberg data architectures, XBRL datasets, and performance-optimized structures such as materialized viewsโ€”ensuring data accuracy, integrity, and trust across the enterprise. This role also requires proficiency in AI tools and AI-driven workflows, leveraging automation and intelligent testing techniques to improve quality and delivery speed.
This opportunity is 100% remote.ย 
Key Responsibilities
Test Automation & QA Engineering
  • Design, develop, and maintain automated QA frameworks for data pipelines, APIs, and analytics platforms using Python and SQL.
  • Build reusable testing utilities for data validation, regression testing, and pipeline certification.
  • Integrate automated tests into CI/CD pipelines to support continuous testing and deployment.
  • Develop unit, integration, and end-to-end test cases for complex data workflows.
  • Leverage AI-assisted testing tools to generate test cases, identify edge cases, and improve test coverage.
Data Validation & ETL Testing
  • Validate ETL/ELT pipelines to ensure accurate ingestion, transformation, and delivery of data.
  • Create automated checks for data completeness, consistency, accuracy, and timeliness.
  • Test ingestion and transformation of complex datasets, including XBRL financial data.
  • Implement reconciliation and audit mechanisms across source-to-target mappings.
  • Apply AI-driven anomaly detection to identify data quality issues and pipeline failures.
Iceberg & Materialized View Testing
  • Develop and execute test strategies for Apache Iceberg-based data lakehouse architectures, including:
    • Schema evolution validation
    • Time travel and versioning accuracy
    • Partitioning and performance behavior
  • Validate and compare materialized views vs. Iceberg table performance and consistency, including:
    • Query performance benchmarking
    • Data freshness and latency
    • Storage efficiency and maintenance overhead
  • Ensure alignment between precomputed datasets (materialized views) and underlying source data.
Data Quality, Metadata & Context Validation
  • Implement automated validation for data quality rules, lineage, and metadata accuracy.
  • Support context engineering by validating that datasets include proper business context, definitions, and relationships.
  • Integrate QA processes with enterprise data catalogs and metadata systems to ensure discoverability and trust.
  • Validate AI-generated metadata, lineage, and transformations for accuracy and traceability.
AI-Driven Quality Engineering
  • Apply AI/ML and generative AI tools to enhance QA processes, including intelligent test generation, defect prediction, and automated root cause analysis.
  • Validate data readiness for AI/ML and generative AI use cases, ensuring datasets meet quality, completeness, and governance standards.
  • Collaborate with data and AI teams to test data pipelines supporting RAG, analytics, and machine learning workflows.
  • Ensure alignment with responsible AI practices, including traceability, explainability, and data integrity.
OCDO & Data Strategy Support
  • Support enterprise data management programs and OCDO initiatives by ensuring data quality and reliability across systems.
  • Contribute to data maturity assessments by evaluating data quality, testing coverage, and governance adherence.
  • Align QA processes with Federal Data Strategy and Evidence Act requirements.
Stakeholder Collaboration & Agile Delivery
  • Work closely with data engineers, data architects, and analysts to define test strategies and acceptance criteria.
  • Participate in stakeholder engagement sessions and listening campaigns to understand data quality expectations and pain points.
  • Document test results, defects, and quality metrics for both technical and non-technical stakeholders.
  • Operate within Agile teams to iteratively improve data quality processes and tooling.
  • Promote adoption of AI-driven efficiencies and automation across QA and data engineering workflows.
Required Qualifications
  • Bachelorโ€™s degree in Computer Science, Engineering, Information Systems, or related field.
  • 5+ years of experience in QA engineering, data testing, or software development.
  • Strong programming skills in Python and advanced proficiency in SQL.
  • Experience building automated test frameworks for data platforms and ETL pipelines.
  • Hands-on experience with:
    • AWS data services (S3, Glue, Redshift, Lambda, etc.)
    • Apache Iceberg or similar data lake technologies
  • Experience validating materialized views and performance-optimized data structures.
  • Familiarity with XBRL or complex financial/regulatory datasets.
  • Understanding of data modeling, metadata, and data governance principles.
  • Experience with CI/CD tools and automated testing integration.
  • Demonstrated proficiency with AI tools and AI-assisted development/testing workflows.
  • Understanding of data quality requirements for AI/ML and analytics use cases.
  • U.S. Citizenship required; ability to obtain and maintain a federal clearance.
Preferred Qualifications
  • Experience supporting federal agencies such as SEC, DHS, Treasury, or Federal Reserve System.
  • Familiarity with data catalog and governance tools (e.g., Collibra, Alation, ServiceNow).
  • Experience with Apache Spark or distributed data processing frameworks.
  • Knowledge of data quality tools and observability platforms.
  • Exposure to data maturity frameworks (e.g., EDM DCAM, TDWI).
  • Experience testing large-scale cloud data platforms and lakehouse architectures.
  • Experience validating data pipelines supporting AI/ML, analytics, or generative AI solutions.
  • Familiarity with AI-driven testing tools or frameworks.

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